In this paper, sketches are considered as interactive and incremental process rather than collection of strokes or images. It is a polynomial algorithm.
Stroke ordering is an important source of knowledge in recognition which has been used in the paper. This is called sketching style.
Three tasks for sketch recognition:
Segmentation: Grouping strokes so that all those from same object are in one group
Classification: Which object each group represents
Labeling: Assigning labels to components of recognized objects
Dynamic properties of sketches( interactive, incremental and highly stylized) is used in this paper. Analysis was done to find the extent to find the extent to which people have sketching styles. Also sketching style diagrams were constructed.
The user input is encoded to generate observation sequences and this is used to compare to its model of how a user draws.
Modeling with HMM
Encoding: The strokes are converted to give geometric primitives. This is then encoded into sequences using 13 symbols.
Since instances of the same object sketched in different styles may have encodings of different lengths, two frameworks were used - modeling with fixed and variable input length HMM. This is used to segment and classify the objects. It not only gives the best classification but also the the next k-best segmentation.
For testing four domains were used- domains of geometric objects, military course of action diagrams, stick-figure diagrams, and mechanical engineering drawings.
The system does not require that the user finish drawing the current object before it can be run (i.e., it
doesn’t need to be told that user is done sketching). It can be run after each stroke is added to the surface.
My thoughts: Recognizing the drawing even before completing could be a very interesting feature in the application for the user using it. One disadvantage that I think is present in this model is that it is user dependent and needs the system to be trained for the user
Stroke ordering is an important source of knowledge in recognition which has been used in the paper. This is called sketching style.
Three tasks for sketch recognition:
Segmentation: Grouping strokes so that all those from same object are in one group
Classification: Which object each group represents
Labeling: Assigning labels to components of recognized objects
Dynamic properties of sketches( interactive, incremental and highly stylized) is used in this paper. Analysis was done to find the extent to find the extent to which people have sketching styles. Also sketching style diagrams were constructed.
The user input is encoded to generate observation sequences and this is used to compare to its model of how a user draws.
Modeling with HMM
Encoding: The strokes are converted to give geometric primitives. This is then encoded into sequences using 13 symbols.
Since instances of the same object sketched in different styles may have encodings of different lengths, two frameworks were used - modeling with fixed and variable input length HMM. This is used to segment and classify the objects. It not only gives the best classification but also the the next k-best segmentation.
For testing four domains were used- domains of geometric objects, military course of action diagrams, stick-figure diagrams, and mechanical engineering drawings.
The system does not require that the user finish drawing the current object before it can be run (i.e., it
doesn’t need to be told that user is done sketching). It can be run after each stroke is added to the surface.
My thoughts: Recognizing the drawing even before completing could be a very interesting feature in the application for the user using it. One disadvantage that I think is present in this model is that it is user dependent and needs the system to be trained for the user
What about the example and how it detected strokes?
ReplyDelete